ABSTRACT
The emergence of severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) reawakened the need to rapidly understand the molecular etiologies, pandemic potential, and prospective treatments of infectious agents. The lack of existing data on SARS-CoV-2 hampered early attempts to treat severe forms of coronavirus disease-2019 (COVID-19) during the pandemic. This study coupled existing transcriptomic data from severe acute respiratory syndrome-related coronavirus 1 (SARS-CoV-1) lung infection animal studies with crowdsourcing statistical approaches to derive temporal meta-signatures of host responses during early viral accumulation and subsequent clearance stages. Unsupervised and supervised machine learning approaches identified top dysregulated genes and potential biomarkers (e.g. CXCL10, BEX2, and ADM). Temporal meta-signatures revealed distinct gene expression programs with biological implications to a series of host responses underlying sustained Cxcl10 expression and Stat signaling. Cell cycle switched from G1/G0 phase genes, early in infection, to a G2/M gene signature during late infection that correlated with the enrichment of DNA damage response and repair genes. The SARS-CoV-1 meta-signatures were shown to closely emulate human SARS-CoV-2 host responses from emerging RNAseq, single cell, and proteomics data with early monocyte-macrophage activation followed by lymphocyte proliferation. The circulatory hormone adrenomedullin was observed as maximally elevated in elderly patients who died from COVID-19. Stage-specific correlations to compounds with potential to treat COVID-19 and future coronavirus infections were in part validated by a subset of twenty-four that are in clinical trials to treat COVID-19. This study represents a roadmap to leverage existing data in the public domain to derive novel molecular and biological insights and potential treatments to emerging human pathogens.
ABSTRACT
Copy number variant (CNV) analysis was performed on renal cell carcinoma (RCC) specimens (chromophobe, clear cell, oncocytoma, papillary type 1, and papillary type 2) using high-resolution arrays (1.85 million probes). The RCC samples exhibited diverse genomic changes within and across tumor types, ranging from 106 to 2238 CNV segments in a clear-cell specimen and in a papillary type 2 specimen, respectively. Despite this heterogeneity, distinct CNV segments were common within each tumor classification: chromophobe (seven segments), clear cell (three segments), oncocytoma (nine segments), and papillary type 2 (two segments). Shared segments ranged from a 6.1-kb deletion (oncocytomas) to a 208.3-kb deletion (chromophobes). Among common tumor type-specific variations, chromophobes, clear-cell tumors, and oncocytomas were composed exclusively of noncoding DNA. No CNV regions were common to papillary type 1 specimens, although there were 12 amplifications and 12 deletions in five of six samples. Three microRNAs and 12 mRNA genes had a ≥98% coding region contained within CNV regions, including multiple gene families (chromophobe: amylases 1A, 1B, and 1C; oncocytoma: general transcription factors 2H2, 2B, 2C, and 2D). Gene deletions involved in histone modification and chromatin remodeling affected individual subtypes (clear cell: SFMBT and SETD2; papillary type 2: BAZ1A) and the collective RCC group (KDM4C). The genomic amplifications/deletions identified herein represent potential diagnostic and/or prognostic biomarkers.
